Our aim is to compute the minimal data-set implied by the assertions of the English language, extract it from the database, and store it in files of our own format. Towards this direction we read the table of assertions (conceptnet assertion) and keep the entries that have their language id set to en. According to Table A.1 in Appendix A, every assertion is associated with entries from the database tables conceptnet concept (Table A.2), conceptnet relation (Table A.3), nl frequency (Table A.4), conceptnet frame (Table A.5), conceptnet surfaceform (Table A.6), and conceptnet rawassertion (Table A.7). Through conceptnet rawassertion the assertions are also associated with the actual sentences which are located in the table corpus sentence (Table A.6). Moreover, we do not need any other table from the database, as the important entries from all the above tables are contained in among these tables. It turns out that reading once the assertions and then all the entries referenced from the assertions in the English language is not enough to produce a minimal consistent data-set. Section 1.1 explains why, and gives a high-level overview of the process that we follow in order to compute the closure of the data-set implied by the assertions of the English language. However, before we describe these reasons we mention which fields we are going to keep from each table of the original ConceptNet 4 database.

Mining companies have complex supply chains that start from the mining location and stretch thousands of kilometers to the end customer in a different country and continent. The logistics of moving the materials from mines to ship is composed of series of optimization problems like berth allocation , ship scheduling , stockyard scheduling , and rail scheduling , which are individually NP-hard. In this paper, we present a scheduling application, called as IBM Optimization: Mine to Ship , for end-to-end integrated operations scheduling. The application is built on IBM ILOG ODM Enterprise with advanced features like rescheduling under deviations and disturbances, and maintenance scheduling. The modeling and computational complexity of integrated scheduling optimization is tamed using hybrid optimization technique that leverages mathematical programming and constraint programming. The application will benefit the mining companies with increased resource usage, higher throughput, reduced cost of operations, and higher revenue.

Although the use of metric fluents is fundamental to many practical planning problems, the study of heuristics to support fully automated planners working with these fluents remains relatively unexplored. The most widely used heuristic is the relaxation of metric fluents into interval-valued variables --- an idea first proposed a decade ago. Other heuristics depend on domain encodings that supply additional information about fluents, such as capacity constraints or other resource-related annotations. A particular challenge to these approaches is in handling interactions between metric fluents that represent exchange, such as the transformation of quantities of raw materials into quantities of processed goods, or trading of money for materials. The usual relaxation of metric fluents is often very poor in these situations, since it does not recognise that resources, once spent, are no longer available to be spent again. We present a heuristic for numeric planning problems building on the propositional relaxed planning graph, but using a mathematical program for numeric reasoning. We define a class of producer--consumer planning problems and demonstrate how the numeric constraints in these can be modelled in a mixed integer program (MIP). This MIP is then combined with a metric Relaxed Planning Graph (RPG) heuristic to produce an integrated hybrid heuristic. The MIP tracks resource use more accurately than the usual relaxation, but relaxes the ordering of actions, while the RPG captures the causal propositional aspects of the problem. We discuss how these two components interact to produce a single unified heuristic and go on to explore how further numeric features of planning problems can be integrated into the MIP. We show that encoding a limited subset of the propositional problem to augment the MIP can yield more accurate guidance, partly by exploiting structure such as propositional landmarks and propositional resources. Our results show that the use of this heuristic enhances scalability on problems where numeric resource interaction is key in finding a solution.

In this paper Knowledge Discovery System (KDS) is proposed and implemented for the extraction of knowledge-mean stiffness of a polymer composite material in which when fibers are placed at different orientations. Cosine amplitude method is implemented for retrieving compatible polymer matrix and reinforcement fiber which is coming under predicted fiber class, from the polymer and reinforcement database respectively, based on the design requirements. Fuzzy classification rules to classify fibers into short, medium and long fiber classes are derived based on the fiber length and the computed or derive critical length of fiber. Longitudinal and Transverse module of Polymer Matrix Composite consisting of seven layers with different fiber volume fractions and different fibers orientations at 0,15,30,45,60,75 and 90 degrees are analyzed through Rule-of Mixture material design model. The analysis results are represented in different graphical steps and have been measured with statistical parameters. This data mining application implemented here has focused the mechanical problems of material design and analysis. Therefore, this system is an expert decision support system for optimizing the materials performance for designing light-weight and strong, and cost effective polymer composite materials.

Advanced engineering materials design involves the exploration of massive multidimensional feature spaces, the correlation of materials properties and the processing parameters derived from disparate sources. The search for alternative materials or processing property strategies, whether through analytical, experimental or simulation approaches, has been a slow and arduous task, punctuated by infrequent and often expected discoveries. A few systematic efforts have been made to analyze the trends in data as a basis for classifications and predictions. This is particularly due to the lack of large amounts of organized data and more importantly the challenging of shifting through them in a timely and efficient manner. The application of recent advances in Data Mining on materials informatics is the state of art of computational and experimental approaches for materials discovery. In this paper similarity based engineering materials selection model is proposed and implemented to select engineering materials based on the composite materials constraints. The result reviewed from this model is sustainable for effective decision making in advanced engineering materials design applications.

The Reverse Water Gas Shift system (RWGS) is a complex physical system designed to produce oxygen from the carbon dioxide atmosphere on Mars. If sent to Mars, it would operate without human supervision, thus requiring a reliable automated system for monitoring and control. The RWGS presents many challenges typical of real-world systems, including: noisy and biased sensors, nonlinear behavior, effects that are manifested over different time granularities, and unobservability of many important quantities. In this paper we model the RWGS using a hybrid (discrete/continuous) Dynamic Bayesian Network (DBN), where the state at each time slice contains 33 discrete and 184 continuous variables. We show how the system state can be tracked using probabilistic inference over the model. We discuss how to deal with the various challenges presented by the RWGS, providing a suite of techniques that are likely to be useful in a wide range of applications. In particular, we describe a general framework for dealing with nonlinear behavior using numerical integration techniques, extending the successful Unscented Filter. We also show how to use a fixed-point computation to deal with effects that develop at different time scales, specifically rapid changes occurring during slowly changing processes. We test our model using real data collected from the RWGS, demonstrating the feasibility of hybrid DBNs for monitoring complex real-world physical systems.

We explore applicability of the situation calculus, the well-known logical framework developed in Artificial Intelligence for representation of dynamic systems, to the task of representing knowledge about processes, actions and events in the natural sciences. In this paper, we concentrate on a case study in the area of organic chemistry. More specifically, we adapt the situation calculus to the task of automating organic synthesis planning on a qualitative level, where the objective is to identify a chain of chemical reactions transforming the given initial molecules into the desired goal molecule. We present two approaches for reasoning about reactions in organic chemistry: a “micro” approach and a “macro” approach. The “micro” approach is a low level approach that explicitly represents the most elementary interactions between molecules during a single chemical reaction, namely the splitting and forming of bonds between atoms. In contrast, the“macro” approach is a higher level approach that treats each chemical reaction (a set of splits and formation of bonds) as an elementary action. Both approaches are implemented in PROLOG. Declarative heuristics are defined to reduce the search space and help the program to find the correct synthesis routes more quickly. We hope that the lessons learned from our successful case study can have discovery potential in other bio-medical sciences. We discuss briefly how the proposed approaches can contribute to solving other research problems and to communicating pathways.

By elaborating on the notion of linear belief functions (Dempster 1990; Liu 1996), we propose an elementary approach to knowledge representation for expert systems using linear belief functions. We show how to use basic matrices to represent market information and financial knowledge, including complete ignorance, statistical observations, subjective speculations, distributional assumptions, linear relations, and empirical asset pricing models. We then appeal to Dempster's rule of combination to integrate the knowledge for assessing an overall belief of portfolio performance, and updating the belief by incorporating additional information. We use an example of three gold stocks to illustrate the approach.

The production of any new medicine requires solutions to many planning problems. The most fundamental of these is determining the sequence of chemical reactions necessary to physically create the drug. Surprisingly, these organic syntheses can be modeled as branching paths in a discrete, fully-observable state space, making the construction of new medicines an application of heuristic search. We describe a model of organic chemistry that is amenable to traditional AI techniques from game tree search, regression, and automatic assembly sequencing. We demonstrate the applicability of AND/OR graph search by developing the first chemistry solver to use proof-number search. Finally, we construct a benchmark suite of organic synthesis problems collected from undergraduate organic chemistry exams, and we analyze our solvers performance both on this suite and in recreating the synthetic plan for a multibillion dollar drug.

This paper describes a polynomially-solvable sub-problem of temporal planning. Polynomiality follows from two assumptions. Firstly, by supposing that each sub-goal fluent can be established by at most one action, we can quickly determine which actions are necessary in any plan. Secondly, the monotonicity of sub-goal fluents allows us to express planning as an instance of STP≠ (Simple Temporal Problem, difference constraints). Our class includes temporally-expressive problems, which we illustrate with an example of chemical process planning.